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<TITLE> Introduction</TITLE>
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<B> Next:</B> <!WA6><A NAME=tex2html25 HREF="http://www-cse.ucsd.edu/users/rik/section3_2.html"> Adaptive information retrieval</A>
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<H1><A NAME=SECTION0001000000000000000> Introduction</A></H1>
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My research focus is a characterization of adaptive knowledge
representations.  Issues of representation have always played a
central role in artificial intelligence (AI), as well as in computer
science and theories of mind more generally.  But I would argue that
most of this work has (implicitly or explicitly) assumed that the
representational language is wielded <em>manually</em>, by humans
encoding an explicit characterization of what they believe to be true
of the world.  Philosophical difficulties aside, some modern machine
learning techniques are capable of <em>automatically</em> developing
elaborate representations of the world.  A central result of the
mathematical theory of induction is that the selection of an
appropriate language for representing learned concepts is absolutely
critical to their identification.  It is therefore appropriate to
reconsider basic notions of what makes for good knowledge
representation, with constraints imposed by the learning process
considered <em>sine qua non</em>, along with those (expressive adequacy,
valid inference, etc.)  more typically considered by AI.
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I have found it productive to pursue this general interest through
two more specific research projects.  The first of these uses
connectionist (neural) networks as a representation for the
information retrieval (IR) problem.  This construction allows an IR
system to learn a more effective indexing representation of free-text
documents as a simple by-product of the browsing behaviors of its
users.  Second and more recently, I have investigated Genetic
Algorithm (GAs), particularly interactions between neural networks and
the GA, both as algorithmic techniques and as models of natural
phenomena (learning and evolution, resp.).  I have found that my work
in these two areas allows a ``stereoscopic'' view, encompassing both
low-level biological constraints and high-level cultural issues, that
are at the heart of modern AI and cognitive science.
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<P><ADDRESS>
rik@cs.ucsd.edu
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